Comparison of the Spatio-Temporal Variability of Annual Minimum Daily Extreme Flow Characteristics as a Function of Land Use and Dam Management Mode in Quebec, Canada
Bibliographic record
Abstract
This study presents a comparison of the spatio-temporal variability of characteristics (magnitude, duration and timing) of annual minimum daily extreme flows (AMEF) as a function of land use and the mode of management of dams. Streamflow measured at stations not affected by dams at Joliette, along the L’Assomption River (agricultural watershed, 1340 km2), and at Saint-Michel-des-Saints, on the Matawin River (forested watershed, 1390 km2) on one hand, and downstream from the Rawdon dam (regulated natural-type management mode), on the Ouareau River (1260 km2), which is the main tributary of the L’Assomption River, and from the Matawin dam (inverted-type management mode), on the Matawin River (4070 km2), on the other hand, were compared over the period from 1930 to 2010. As far as the spatial variability of natural rivers is concerned, the magnitude and duration of AMEF are higher in the forested watershed than in the agricultural watershed. In regulated rivers, AMEF magnitude is higher downstream from the dam characterized by a natural-type management mode than downstream from the dam characterized by inversion-type management. However, downstream from the latter, AMEF occur much more frequently and very early in the year. As for temporal variability, the Lombard method did not reveal any influence of land use differences on the stationarity of series of AMEF characteristics. In contrast, differences in dam management mode result in occurrences of AMEF downstream from the inversion-type dam progressively earlier in the year. The duration and timing of AMEF are not correlated with the same climate variables, be it in natural rivers or downstream from dams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".